AI in Cardiac Imaging and IC and supine stress MPI in comparison with TPD.34 Similarly, the area under the receiver-operating curve for prediction of disease per patient and per vessel by the ML algorithm was better (per patient, 0.81 versus 0.78; per vessel 0.77 versus 0.73, p<0.001).34 Arsajani evaluated the combination of clinical and imaging data to predict CAD using SPECT.35 The receiveroperating curve for the ML method was better than TPD and two readers with considerable significance (p<0.001). Arsajani et al. examined clinical and imaging data to determine the probability of revascularisation in 713 patients with suspected CAD through a LogitBoost supervised learning algorithm.36 The specificity of the ML algorithm was better than both expert readers (p<0.05) and similar to total perfusion deficit (p<0.05). In addition, the receiver-operating curve for the ML architecture (0.81 ± 0.02) was similar to reader 1 (0.81 ± 0.02), but better than reader 2 (0.72 ± 0.02; p<0.01) and the standalone measure of perfusion (0.77 ± 0.02; p<0.01). Alonso et al. used a supervised ML algorithm to estimate the risk of cardiac death, derived from an amalgamation of adenosine myocardial perfusion SPECT with clinical information in 8,321 patients and 551 cases of cardiac death.37 The logistic regression was outperformed by the ML algorithm (AUC 0.76; 14 features). It evidently showed superior accuracy (AUC 0.83; p<0.0001; 49 features). Nonetheless, the least absolute shrinkage and selection operator (LASSO) model required the least number of features (AUC 0.77; p=0.045; six features). Betancur et al. explored the predictive value of patient information with SPECT MPI to predict MACE through a LogitBoost supervised learning ML algorithm in 2,619 patients.38 At around 3 years’ follow-up, 239 patients had experienced MACE. Interestingly, the ML combined achieved superior MACE prediction than ML imaging (AUC 0.81 versus 0.78; p<0.01). The ML also had higher MACE predictive accuracy when compared with an expert reader, automated stress total perfusion deficit and automated ischaemic perfusion deficit (AUC 0.81 versus 0.65 versus 0.73 versus 0.71; p<0.01 for all). Role of AI in Cardiac CT CT is a non-invasive approach for the identification of obstructive CAD. CT enables the depiction of the underlying coronary anatomy to visualise plaques or stenosis in the coronary artery tree.39 From an interventionist point of view, cardiac CT plays an important role in appropriate selection for PCI. Among all non-invasive modalities, CT angiography closely mirrors invasive angiography. ML algorithms can greatly augment the possibilities of cardiac CT. Lessmann et al. used a convolutional neural network (CNN), a deep learning algorithm, to create a bounding box around the heart that corresponds to certain Hounsfield units.40 They investigated the potential of an automated coronary calcium system that was able/would be to screen patients for high-risk cardiovascular events. Santini et al. explored the role of a CNN algorithm in classifying and segmenting lesions in cardiac CT imaging.41 After proper training of the CNN algorithm with various CT volumes, they were able to demonstrate a Pearson correlation measuring 0.983. Zreik et al. used a CNN algorithm to automatically calculate the fractional flow reserve (FFR) from coronary CT angiography.42 Surprisingly, there was good agreement between the ML-derived values and invasively measured one, with the AUC being 0.74. Rosendael et al. examined the role of a boost ensemble algorithm to compare ML-derived scores and current risk scores in CT angiography for CAD evaluation.43 The events expressed by the AUC was superior by the ML algorithm in reference to conventional scores (0.771 versus 0.685 to 0.701; p<0.001). Role of AI in Cardiac MRI Cardiovascular magnetic resonance (CMR) imaging has emerged as a robust diagnostic modality for assessing a variety of clinical conditions in cardiology. Of the various non-invasive approaches, it is the only option that permits tissue characterisation. CMR can be used by the interventionist to plan appropriate management for patients. During procedures, CMR imaging can be used to guide complex procedures because of the minimal risk of radiation. Tan et al. assessed the role of the CNN algorithm for automatic segmentation of the left ventricle in short-axis slices in publicly available database.44 Interestingly, the ML algorithm achieved a Jaccard index of 0.77 for the left ventricle segmentation challenge dataset and obtained a continuously ranked probability score of 0.0124 for the Kaggle Second Annual Data Science Bowl. Similarly, Ruijinsk et al. evaluated the role of the CNN framework for automated ventricular function assessment from cardiac CMR.45 The findings corroborated highly with manual analysis for left ventricular and right ventricular volumes (all r >0.95), strain (circumferential r = 0.89; longitudinal r >0.89) and ejection rates (all r ≥0.93). Can AI-integrated Cardiovascular Imaging Facilitate Interventional Therapies? Using the vast troves of cardiovascular imaging enables the possibilities of data-driven phenotypic differentiation.46 This has particular relevance in the field of IC and transcatheter therapies for enabling individualised therapies.5 Algorithms integrating ML and cardiovascular imaging can help generate patient-specific risk scores, which can yield diagnostic significance in procedural planning.14 Furthermore, ML may play a paramount role in automating cardiovascular imaging workflow for referral of patients to cardiac catherisation laboratory by facilitating faster reading, interpretation and diagnosis.5,14 In addition, ML algorithms may help predict outcomes such as mortality or complications following interventional therapies. Role of AI in Percutaneous Coronary Intervention and Transcatheter Aortic Valve Replacement Although the potential of AI has not been fully harnessed in PCI, a few studies provide a glimpse of AI in the near future.14 One aspect of considerable variability in interpretation is physiologically guided coronary revascularisation.8 The consistency of results can be improved with AI. Cook et al. compared an AI algorithm with 15 human experts for interpretation of 1,008 instantaneous wave-free ratio (iFR) pressure-wire pullback traces.13 In addition, a heart team interpretation was determined by a consensus of individual opinions. The median human expert had an 89.3% agreement with heart team response, while it was 89.4% with AI (p<0.01 for non-inferiority) for PCI haemodynamic appropriateness. Within the 372 cases evaluated for haemodynamic appropriateness, the AI framework had 89.7% agreement while the median human expert was 88.8% in agreement with the heart team response (p<0.01 for non-inferiority). Cook et al. confirmed that the AI algorithm was not inferior to the expert decision making for determining the appropriateness and strategy of PCI.13 Azzalini et al. used a generalised boost regression to determine which contrast media among five types was associated with contrast-induced INTERVENTIONAL CARDIOLOGY: REVIEWS, RESEARCH, RESOURCES Access at: www.ICRjournal.com